A NON-STATIONARY HIDDEN MARKOV MODEL OF MULTIVIEW VIDEO TRAFFIC

被引：7

作者：

Rossi, Lorenzo ^{[1
]}

Chakareski, Jacob ^{[2
]}

Frossard, Pascal ^{[2
]}

Colonnese, Stefania ^{[1
]}

机构：

[1] Univ Roma La Sapienza, INFOCOM Dept, Via Eudossiana 18, I-00184 Rome, Italy

[2] Ecole Polytech Fed Lausanne, LTS4, CH-1015 Lausanne, Switzerland

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING | 2010年

基金：

瑞士国家科学基金会;

关键词：

D O I：

10.1109/ICIP.2010.5652844

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Multiview video is increasingly getting attention due to emerging applications such as 3DTV and immersive teleconferencing. In this paper, we present a non-stationary Hidden Markov Model (HMM) for characterizing the data rate of compressed multiview content. The states of the model correspond to different video activity levels and exhibit a Poisson state duration distribution. We derive a stable maximum likelihood algorithm for estimating the parameters of our multiview traffic model. Synthetic data generated by the model exhibits statistics that closely match those of actual multiview data. In addition, we demonstrate the high accuracy of the model in two multiview streaming applications by evaluating the frame loss rate of a constrained network buffer fed by actual and synthetic data.

引用

页码：2921 / 2924

页数：4

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